Machine Learning in Geohazard Risk Prediction and Assessment

From Microscale Analysis to Regional Mapping

Biswajeet Pradhan, Daichao Sheng, Xuzhen He

• Publisher: Elsevier
• Year: 2025
• Topic: Biological Sciences
• eBook ISBN:
  9780443236648
• Subtopic: Environmental Science
• Index: Biological Sciences

Machine Learning in Geohazard Risk Prediction and Assessment: From Microscale Analysis to Regional Mapping presents an overview of the most recent developments in machine learning techniques that have reshaped our understanding of geo-materials and management protocols of geo-risk. The book covers a broad category of research on machine-learning techniques that can be applied, from microscopic modeling to constitutive modeling, to physics-based numerical modeling, to regional susceptibility mapping. This is a good reference for researchers, academicians, graduate and undergraduate students, professionals, and practitioners in the field of geotechnical engineering and applied geology. - Introduces machine-learning techniques in the risk management of geo-hazards, particularly recent developments - Covers a broader category of research and machine-learning techniques that can be applied, from microscopic modeling to constitutive modeling, to physics-based numerical modeling, to regional susceptibility mapping - Contains contributions from top researchers around the world, including authors from the UK, USA, Australia, Austria, China, and India

Table of contents

• Front Cover
• Machine Learning in Geohazard Risk Prediction and Assessment
• Copyright Page
• Contents
• List of contributors
• About the editors
• Preface
• 1 Machine learning methods and connections between different parts
• 2 Machine learning in constitutive modelling of geo-materials
• 3 Machine learning in numerical modelling of geotechnical problems
• 4 Machine learning in data-drivin geotechnical engineering
• Index
• Back Cover